code: def _load_runnables(path, conf):
      model_type = conf.get(_MODEL_TYPE_KEY)
      model_data = conf.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME)
      if model_type in (
          x.__name__ for x in lc_runnable_with_steps_types()
      ):
          return _load_runnable_with_steps(
              os.path.join(path, model_data), model_type
          )
      if (
          model_type in (x.__name__ for x in picklable_runnable_types())
          or model_data == _MODEL_DATA_PKL_FILE_NAME
      ):
          return _load_from_pickle(os.path.join(path, model_data))
      if model_type in (x.__name__ for x in lc_runnable_branch_type()):
          return _load_runnable_branch(
              os.path.join(path, model_data), model_type
          )
      raise MlflowException.invalid_parameter_value(
          _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(
              instance_type=model_type
          )
      )
  
file_path: mlflow/mlflow/langchain/runnables.py
imports: import os
  from pathlib import Path
  from typing import Union
  import cloudpickle
  import yaml
  from mlflow.exceptions import MlflowException
  from mlflow.langchain.utils import _BASE_LOAD_KEY, _CONFIG_LOAD_KEY, _MODEL_DATA_FOLDER_NAME, _MODEL_DATA_KEY, _MODEL_DATA_PKL_FILE_NAME, _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY, _MODEL_TYPE_KEY, _RUNNABLE_LOAD_KEY, _UNSUPPORTED_MODEL_ERROR_MESSAGE, _load_base_lcs, _load_from_json, _load_from_pickle, _load_from_yaml, _save_base_lcs, _validate_and_wrap_lc_model, base_lc_types, custom_type_to_loader_dict, lc_runnable_branch_type, lc_runnable_with_steps_types, lc_runnables_types, picklable_runnable_types
label_desc: Use image node_id 11 for calling a global function with example usage: _load_runnables(path, conf) and returns: _load_runnable_with_steps, _load_from_pickle, _load_runnable_branch